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On cognitive preferences and the plausibility of rule-based models
Machine Learning ( IF 7.5 ) Pub Date : 2019-12-24 , DOI: 10.1007/s10994-019-05856-5 Johannes Fürnkranz , Tomáš Kliegr , Heiko Paulheim
Machine Learning ( IF 7.5 ) Pub Date : 2019-12-24 , DOI: 10.1007/s10994-019-05856-5 Johannes Fürnkranz , Tomáš Kliegr , Heiko Paulheim
It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that—all other things being equal—longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowdsourcing study based on about 3000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recognition heuristic, and investigate their relation to rule length and plausibility.
中文翻译:
关于认知偏好和基于规则的模型的合理性
机器学习和数据挖掘中的传统观点是,规则集等逻辑模型比其他模型更具可解释性,并且在此类基于规则的模型中,更简单的模型比更复杂的模型更具可解释性。在本立场文件中,我们通过关注可解释性的一个特定方面(即模型的合理性)来质疑后一种假设。粗略地说,我们将模型的合理性等同于用户接受它作为预测解释的可能性。特别是,我们认为——在所有其他条件相同的情况下——较长的解释可能比较短的解释更有说服力,而且较短模型的主要偏见,这通常是学习强大的判别模型所必需的,但在涉及到用户对学习模型的接受程度。为此,我们首先概括支持和反对这一假设的证据,然后在基于大约 3000 条判断的众包研究中报告评估结果。结果并未显示出对简单规则的强烈偏好,而我们可以观察到在某些领域中对较长规则的偏好较弱。然后,我们将这些结果与众所周知的认知偏差(例如合取谬误、代表性启发式或识别启发式)联系起来,并研究它们与规则长度和合理性的关系。
更新日期:2019-12-24
中文翻译:
关于认知偏好和基于规则的模型的合理性
机器学习和数据挖掘中的传统观点是,规则集等逻辑模型比其他模型更具可解释性,并且在此类基于规则的模型中,更简单的模型比更复杂的模型更具可解释性。在本立场文件中,我们通过关注可解释性的一个特定方面(即模型的合理性)来质疑后一种假设。粗略地说,我们将模型的合理性等同于用户接受它作为预测解释的可能性。特别是,我们认为——在所有其他条件相同的情况下——较长的解释可能比较短的解释更有说服力,而且较短模型的主要偏见,这通常是学习强大的判别模型所必需的,但在涉及到用户对学习模型的接受程度。为此,我们首先概括支持和反对这一假设的证据,然后在基于大约 3000 条判断的众包研究中报告评估结果。结果并未显示出对简单规则的强烈偏好,而我们可以观察到在某些领域中对较长规则的偏好较弱。然后,我们将这些结果与众所周知的认知偏差(例如合取谬误、代表性启发式或识别启发式)联系起来,并研究它们与规则长度和合理性的关系。